Prior approaches to disease progression analysis use complex proprietary data, while the present invention utilizes publically available data and does not require access to any proprietary data.                U.S. patent application Ser. No. 11/503,393 (David Eddy, et al.) discloses a method for simulating a clinical trial includes: selecting a trial procedure for a simulated trial corresponding to the clinical trial; generating a population of subjects for the simulated trial; searching the population of subjects to determine acceptable subjects for the simulated trial; selecting subjects for the simulated trial from the acceptable subjects; simulating the trial procedure for the selected subjects; and collecting trial data for the simulated trial from the simulated trial procedure.        U.S. patent application Ser. No. 12/788,242 (David Eddy, et. al.) discloses a method of determining a quality of care provided by a healthcare provider to individuals in a population is provided. A data processing apparatus that has one or more processors is disclosed. Data representing biomarkers for individuals in a population is received. Baseline and present risks are determined. Risk reduction values are determined. Based on the current risk reduction, a quality score is determined. A scale is created, and the quality score is mapped to the scale. The global quality score of the disclosure provides numerous benefits over past performance measures.        U.S. Pat. No. 8,224,665 (Macdonald Morris) discloses a method and apparatus for predicting a health benefit for an individual is provided. Outcomes from a first simulation on a set of simulated individuals reflecting a population are stored and used to determine a first risk function and corresponding cost values. Outcomes from a second simulation on a set of simulated individuals reflecting having a healthcare intervention are stored and used to determine a second risk function reflecting the intervention and corresponding cost values of the intervention. A benefit function is derived from the difference of the first and second risk functions. A cost function that describes the cost of the intervention is derived from the respective cost values. The derived benefit function and cost function are used to predict the corresponding benefit and cost of the healthcare intervention for a given individual. Individuals can be ranked by degree of expected benefit.        
The Cardiff Model discloses a method to evaluate the impact of new therapies in a population of T2DM patients, modeling disease progression through the implementation of the UK Prospective Diabetes Study (UKPDS) 68 outcomes equation with the model requiring specification of: age, sex, ethnicity, smoking status and duration of diabetes and model changes to the following modifiable risk factors: total cholesterol, HDL cholesterol, systolic blood pressure, weight and glycosylated hemoglobin (HbA1c). While the time-dependent risk factor profiles are simulated through implementation of equations reported in the UKPDS 68 study, pre-specified HbA1c threshold values may be used to invoke escalation to second- and third-line therapies with costs applied to all predicted complications in the year of occurrence. Healthcare maintenance costs are applied in all subsequent years following non-fatal events with the costs of diabetes-related complications being drawn primarily from UKPDS 65 while baseline utility is modeled using age-dependent mean EQ-5D values in subjects, obtained from the Health Survey for England 2003, with no major complications. Utility decrements associated with predicted complications are drawn primarily from UKPDS 62 with model output including: micro-vascular: retinopathy, neuropathy, nephropathy; and macro-vascular complications: congestive heart failure, myocardial infarction, stroke, ischaemic heart disease; hypoglycaemia, diabetes-specific mortality, all-cause mortality and point estimates, and probabilistic output for cost-effectiveness.
The CDC-RTI Diabetes Cost-Effectiveness Model discloses a method of disease progression and cost-effectiveness for type 2 diabetes, following patients from diagnosis to either death or 95 years of age. The model simulates development of diabetes related complications on three micro-vascular disease paths (nephropathy, neuropathy, and retinopathy) and two macro-vascular disease paths for diabetes screening and pre-diabetes with model outcomes including: disease complications, deaths, costs, and quality-adjusted life years. In the model, progression between disease states is governed by transition probabilities that depend on risk factors—including glycemic level (measured by HbA1c levels), blood pressure, cholesterol, and smoking status—and the duration of diabetes. Interventions affect the transition probabilities and resulting complications. For example, tight glycemic control lowers HbA1c, slowing progression on the micro-vascular complication paths. With slower progression, fewer micro-vascular complications occur, resulting in death being delayed, QALYs increase, with the resulting cost of complications reduced. The model has been used to estimate the cost-effectiveness of treatment interventions for patients with diagnosed diabetes while evaluating optimal resource allocation across interventions; assess whether screening for diabetes is cost-effective; show that lifestyle modification is cost-effective in delaying or preventing diabetes among persons with pre-diabetes; and estimate the cost-effectiveness of screening for pre-diabetes.
The Diabetes and Analysis Modeling Framework model uses established methods to develop the central simulation engine (CSE) that lies at the nucleus of DMAF. The architecture of DMAF has been designed so emerging evidence reported in the literature can be efficiently incorporated into the framework and evaluated for potential impact on immediate and long term outcomes. DMAF captures events occurring in routine patient care through an A1c sub model, bridging between patient-specific A1c, and the incidence of complications while multiplicative factors are taken from A1c vs. time curves from published head-to-head studies of the treatments considered. DMAF also contains treatment transition and scheduling based, by default, the treatment consensus algorithm published by Nathan et al. The transitions between treatment strata are modifiable for sensitivity analysis including the functionality to randomly sample a range of start times for additional treatment.
Disease models predict disease progression within a population. Yet, predictions differ among models and populations while models become outdated and do not account for improvement in treatment and newer medical advances.
Modeling treatment improvement on top of existing models is highly beneficial, while, to a lesser extent, including biomarker change is also beneficial; including both improvements together, i.e. treatment improvement and biomarker change, improves models in many cases.
The Reference Model is currently based on secondary data published in clinical trials, with published risk equations, while no individual data is necessary. Yet it is possible to use individual data and non published risk equations with the model. The use of public data the model uses does not limit it.